bibtype J - Journal Article
ARLID 0351359
utime 20240103194317.6
mtime 20101209235959.9
title (primary) (eng) Note on Generating Orthogonal Polynomials and Their Application in Solving Complicated Polynomial Regression Tasks
specification
page_count 13 s.
serial
ARLID cav_un_epca*0351358
ISSN 0974-5718
title International Journal of Mathematics and Computation
volume_id 7
volume 10 (2010)
page_num 48-60
keyword polynomial regression
keyword orthogonalization
keyword numerical methods
keyword markers
keyword biomarkers
author (primary)
ARLID cav_un_auth*0050983
name1 Knížek
name2 J.
country CZ
garant G
author
ARLID cav_un_auth*0100847
name1 Tichý
name2 Petr
full_dept (cz) Oddělení výpočetních metod
full_dept Department of Computational Methods
institution UIVT-O
full_dept Department of Computational Mathematics
fullinstit Ústav informatiky AV ČR, v. v. i.
author
ARLID cav_un_auth*0245237
name1 Beránek
name2 L.
country CZ
author
ARLID cav_un_auth*0101205
name1 Šindelář
name2 Jan
full_dept (cz) Stochastická informatika
full_dept Department of Stochastic Informatics
department (cz) SI
department SI
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0019922
name1 Vojtěšek
name2 B.
country CZ
author
ARLID cav_un_auth*0266747
name1 Bouchal
name2 P.
country CZ
author
ARLID cav_un_auth*0020284
name1 Nenutil
name2 R.
country CZ
author
ARLID cav_un_auth*0266748
name1 Dedík
name2 O.
country CZ
cas_special
project
project_id NS9812
agency GA MZd
country CZ
ARLID cav_un_auth*0266749
project
project_id GAP304/10/0868
agency GA ČR
country CZ
ARLID cav_un_auth*0266750
research CEZ:AV0Z10300504
research CEZ:AV0Z10750506
abstract (eng) In this paper, we describe efficient algorithms for computing solutions of numerically exacting parts of used complicated polynomial regression tasks. In particular, we use a numerically stable way of generating the values of normalized orthogonal polynomials on a discrete set of points; we use “the Arnoldi algorithm with reorthogonalization”, which is the key ingredient of our approach. The generated vectors can then be considered orthogonal also in finite precision arithmetic (up to a small inaccuracy proportional to machine precision). We then use the special algebraic structure of the covariance matrix to find algebraically the inversion of the matrix of the system of normal equations. Therefore, we do not need to compute numerically the inversion of the covariance matrix and we do not even need to solve the system of normal equations numerically. Some consequences of putting the algorithms mentioned into practice are discussed.
reportyear 2011
RIV BA
permalink http://hdl.handle.net/11104/0191129
arlyear 2010
mrcbU63 cav_un_epca*0351358 International Journal of Mathematics and Computation 0974-5718 Roč. 7 č. 10 2010 48 60